






"""simple docstring"""


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__SCREAMING_SNAKE_CASE   =   logging.getLogger(__name__)




@dataclass
class 						__UpperCamelCase     :
					lowercase_    :				str      =  field(
					    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}						)
					lowercase_    :				Optional[str]      =  field(
					    default=UpperCamelCase   ,     metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}						)
					lowercase_    :				Optional[str]      =  field(
					    default=UpperCamelCase   ,     metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}						)
					lowercase_    :				Optional[str]      =  field(
					    default=UpperCamelCase   ,     metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}   ,     )
					lowercase_    :				bool      =  field(default=UpperCamelCase   ,     metadata={"""help""": """Whether tp freeze the encoder."""}						)
					lowercase_    :				bool      =  field(default=UpperCamelCase   ,     metadata={"""help""": """Whether  to freeze the embeddings."""}						)




@dataclass
class 						__UpperCamelCase     :
					lowercase_    :				str      =  field(
					    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}						)
					lowercase_    :				Optional[str]      =  field(
					    default="""summarization"""   ,     metadata={"""help""": """Task name, summarization (or summarization_{dataset} for pegasus) or translation"""}   ,     )
					lowercase_    :				Optional[int]      =  field(
					    default=1024   ,     metadata={
					        """help""": (
					            """The maximum total input sequence length after tokenization. Sequences longer """
					            """than this will be truncated, sequences shorter will be padded."""
					        )
					    }   ,     )
					lowercase_    :				Optional[int]      =  field(
					    default=128   ,     metadata={
					        """help""": (
					            """The maximum total sequence length for target text after tokenization. Sequences longer """
					            """than this will be truncated, sequences shorter will be padded."""
					        )
					    }   ,     )
					lowercase_    :				Optional[int]      =  field(
					    default=142   ,     metadata={
					        """help""": (
					            """The maximum total sequence length for validation target text after tokenization. Sequences longer """
					            """than this will be truncated, sequences shorter will be padded. """
					            """This argument is also used to override the ``max_length`` param of ``model.generate``, which is used """
					            """during ``evaluate`` and ``predict``."""
					        )
					    }   ,     )
					lowercase_    :				Optional[int]      =  field(
					    default=142   ,     metadata={
					        """help""": (
					            """The maximum total sequence length for test target text after tokenization. Sequences longer """
					            """than this will be truncated, sequences shorter will be padded."""
					        )
					    }   ,     )
					lowercase_    :				Optional[int]      =  field(default=-1   ,     metadata={"""help""": """# training examples. -1 means use all."""}						)
					lowercase_    :				Optional[int]      =  field(default=-1   ,     metadata={"""help""": """# validation examples. -1 means use all."""}						)
					lowercase_    :				Optional[int]      =  field(default=-1   ,     metadata={"""help""": """# test examples. -1 means use all."""}						)
					lowercase_    :				Optional[str]      =  field(default=UpperCamelCase   ,     metadata={"""help""": """Source language id for translation."""}						)
					lowercase_    :				Optional[str]      =  field(default=UpperCamelCase   ,     metadata={"""help""": """Target language id for translation."""}						)
					lowercase_    :				Optional[int]      =  field(default=UpperCamelCase   ,     metadata={"""help""": """# num_beams to use for evaluation."""}						)
					lowercase_    :				bool      =  field(
					    default=UpperCamelCase   ,     metadata={"""help""": """If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."""}   ,     )


def        UpperCAmelCase      (					a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						logger.info(F"""***** {split} metrics *****""" )
						for key in sorted(metrics.keys() ):
												logger.info(F"""  {key} = {metrics[key]}""" )
						save_json(a__     ,      os.path.join(a__     ,      F"""{split}_results.json""" ) )


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :str						=			HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )

						if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :List[str]						=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
						else:
												lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Dict						=			parser.parse_args_into_dataclasses()

						check_output_dir(a__ )

						# Setup logging
						logging.basicConfig(
						    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'     ,      datefmt='%m/%d/%Y %H:%M:%S'     ,      level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN     ,      )
						logger.warning(
						    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'     ,      training_args.local_rank     ,      training_args.device     ,      training_args.n_gpu     ,      bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED )     ,      training_args.fpaa     ,      )
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank ):
												transformers.utils.logging.set_verbosity_info()
						logger.info('Training/evaluation parameters %s'     ,      a__ )

						# Set seed
						set_seed(training_args.seed )

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						lowerCAmelCase   :List[str]						=			AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path     ,      cache_dir=model_args.cache_dir     ,      )

						lowerCAmelCase   :List[Any]						=			('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
						for p in extra_model_params:
												if getattr(a__     ,      a__     ,      a__ ):
																		assert hasattr(a__     ,      a__ ), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
																		setattr(a__     ,      a__     ,      getattr(a__     ,      a__ ) )

						lowerCAmelCase   :List[str]						=			AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path     ,      cache_dir=model_args.cache_dir     ,      )
						lowerCAmelCase   :str						=			AutoModelForSeqaSeqLM.from_pretrained(
						    model_args.model_name_or_path     ,      from_tf='.ckpt' in model_args.model_name_or_path     ,      config=a__     ,      cache_dir=model_args.cache_dir     ,      )

						# use task specific params
						use_task_specific_params(a__     ,      data_args.task )

						# set num_beams for evaluation
						if data_args.eval_beams is None:
												lowerCAmelCase   :int						=			model.config.num_beams

						# set decoder_start_token_id for MBart
						if model.config.decoder_start_token_id is None and isinstance(a__     ,      (MBartTokenizer, MBartTokenizerFast) ):
												assert (
												    data_args.tgt_lang is not None and data_args.src_lang is not None
												), "mBart requires --tgt_lang and --src_lang"
												if isinstance(a__     ,      a__ ):
																		lowerCAmelCase   :Tuple						=			tokenizer.lang_code_to_id[data_args.tgt_lang]
												else:
																		lowerCAmelCase   :List[str]						=			tokenizer.convert_tokens_to_ids(data_args.tgt_lang )

						if model_args.freeze_embeds:
												freeze_embeds(a__ )
						if model_args.freeze_encoder:
												freeze_params(model.get_encoder() )
												assert_all_frozen(model.get_encoder() )

						lowerCAmelCase   :Optional[int]						=			SeqaSeqDataset

						# Get datasets
						lowerCAmelCase   :Tuple						=			(
						    dataset_class(
						        a__     ,      type_path='train'     ,      data_dir=data_args.data_dir     ,      n_obs=data_args.n_train     ,      max_target_length=data_args.max_target_length     ,      max_source_length=data_args.max_source_length     ,      prefix=model.config.prefix or ''     ,      )
						    if training_args.do_train
						    else None
						)
						lowerCAmelCase   :Dict						=			(
						    dataset_class(
						        a__     ,      type_path='val'     ,      data_dir=data_args.data_dir     ,      n_obs=data_args.n_val     ,      max_target_length=data_args.val_max_target_length     ,      max_source_length=data_args.max_source_length     ,      prefix=model.config.prefix or ''     ,      )
						    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
						    else None
						)
						lowerCAmelCase   :Tuple						=			(
						    dataset_class(
						        a__     ,      type_path='test'     ,      data_dir=data_args.data_dir     ,      n_obs=data_args.n_test     ,      max_target_length=data_args.test_max_target_length     ,      max_source_length=data_args.max_source_length     ,      prefix=model.config.prefix or ''     ,      )
						    if training_args.do_predict
						    else None
						)

						# Initialize our Trainer
						lowerCAmelCase   :Optional[int]						=			(
						    build_compute_metrics_fn(data_args.task     ,      a__ ) if training_args.predict_with_generate else None
						)
						lowerCAmelCase   :Union[str, Any]						=			SeqaSeqTrainer(
						    model=a__     ,      args=a__     ,      data_args=a__     ,      train_dataset=a__     ,      eval_dataset=a__     ,      data_collator=SeqaSeqDataCollator(
						        a__     ,      a__     ,      model.config.decoder_start_token_id     ,      training_args.tpu_num_cores )     ,      compute_metrics=a__     ,      tokenizer=a__     ,      )

						lowerCAmelCase   :Tuple						=			{}
						# Training
						if training_args.do_train:
												logger.info('*** Train ***' )

												lowerCAmelCase   :List[str]						=			trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
												lowerCAmelCase   :Union[str, Any]						=			train_result.metrics
												lowerCAmelCase   :Union[str, Any]						=			data_args.n_train

												trainer.save_model()  # this also saves the tokenizer

												if trainer.is_world_process_zero():
																		handle_metrics('train'     ,      a__     ,      training_args.output_dir )
																		all_metrics.update(a__ )

																		# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
																		trainer.state.save_to_json(os.path.join(training_args.output_dir     ,      'trainer_state.json' ) )

																		# For convenience, we also re-save the tokenizer to the same directory,
																		# so that you can share your model easily on huggingface.co/models =)
																		tokenizer.save_pretrained(training_args.output_dir )

    # Evaluation
						if training_args.do_eval:
												logger.info('*** Evaluate ***' )

												lowerCAmelCase   :int						=			trainer.evaluate(metric_key_prefix='val' )
												lowerCAmelCase   :int						=			data_args.n_val
												lowerCAmelCase   :int						=			round(metrics['val_loss']     ,      4 )

												if trainer.is_world_process_zero():
																		handle_metrics('val'     ,      a__     ,      training_args.output_dir )
																		all_metrics.update(a__ )

						if training_args.do_predict:
												logger.info('*** Predict ***' )

												lowerCAmelCase   :Optional[Any]						=			trainer.predict(test_dataset=a__     ,      metric_key_prefix='test' )
												lowerCAmelCase   :int						=			test_output.metrics
												lowerCAmelCase   :Optional[int]						=			data_args.n_test

												if trainer.is_world_process_zero():
																		lowerCAmelCase   :str						=			round(metrics['test_loss']     ,      4 )
																		handle_metrics('test'     ,      a__     ,      training_args.output_dir )
																		all_metrics.update(a__ )

																		if training_args.predict_with_generate:
																								lowerCAmelCase   :List[str]						=			tokenizer.batch_decode(
																								    test_output.predictions     ,      skip_special_tokens=a__     ,      clean_up_tokenization_spaces=a__ )
																								lowerCAmelCase   :Dict						=			lmap(str.strip     ,      a__ )
																								write_txt_file(a__     ,      os.path.join(training_args.output_dir     ,      'test_generations.txt' ) )

						if trainer.is_world_process_zero():
												save_json(a__     ,      os.path.join(training_args.output_dir     ,      'all_results.json' ) )

						return all_metrics


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						main()


if __name__ == "__main__":
		main()







"""simple docstring"""


import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests


def        UpperCAmelCase      (					a__     ,      a__=None ):





						'''simple docstring'''






						lowerCAmelCase   :str						=			None
						if token is not None:
												lowerCAmelCase   :List[Any]						=			{'Accept': 'application/vnd.github+json', 'Authorization': F"""Bearer {token}"""}

						lowerCAmelCase   :Dict						=			F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
						lowerCAmelCase   :Optional[Any]						=			requests.get(a__     ,      headers=a__ ).json()
						lowerCAmelCase   :Tuple						=			{}

						try:
												job_links.update({job['name']: job['html_url'] for job in result['jobs']} )
												lowerCAmelCase   :List[Any]						=			math.ceil((result['total_count'] - 1_00) / 1_00 )

												for i in range(a__ ):
																		lowerCAmelCase   :List[Any]						=			requests.get(url + F"""&page={i + 2}"""     ,      headers=a__ ).json()
																		job_links.update({job['name']: job['html_url'] for job in result['jobs']} )

												return job_links
						except Exception:
												print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}""" )

						return {}


def        UpperCAmelCase      (					a__     ,      a__=None ):





						'''simple docstring'''






						lowerCAmelCase   :Optional[Any]						=			None
						if token is not None:
												lowerCAmelCase   :Any						=			{'Accept': 'application/vnd.github+json', 'Authorization': F"""Bearer {token}"""}

						lowerCAmelCase   :Optional[int]						=			F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"""
						lowerCAmelCase   :Any						=			requests.get(a__     ,      headers=a__ ).json()
						lowerCAmelCase   :str						=			{}

						try:
												artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']} )
												lowerCAmelCase   :List[Any]						=			math.ceil((result['total_count'] - 1_00) / 1_00 )

												for i in range(a__ ):
																		lowerCAmelCase   :List[Any]						=			requests.get(url + F"""&page={i + 2}"""     ,      headers=a__ ).json()
																		artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']} )

												return artifacts
						except Exception:
												print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}""" )

						return {}


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Optional[Any]						=			None
						if token is not None:
												lowerCAmelCase   :Optional[Any]						=			{'Accept': 'application/vnd.github+json', 'Authorization': F"""Bearer {token}"""}

						lowerCAmelCase   :Tuple						=			requests.get(a__     ,      headers=a__     ,      allow_redirects=a__ )
						lowerCAmelCase   :Optional[int]						=			result.headers['Location']
						lowerCAmelCase   :int						=			requests.get(a__     ,      allow_redirects=a__ )
						lowerCAmelCase   :Union[str, Any]						=			os.path.join(a__     ,      F"""{artifact_name}.zip""" )
						with open(a__     ,      'wb' ) as fp:
												fp.write(response.content )


def        UpperCAmelCase      (					a__     ,      a__=None ):





						'''simple docstring'''






						lowerCAmelCase   :Optional[Any]						=			[]
						lowerCAmelCase   :Dict						=			[]
						lowerCAmelCase   :Optional[Any]						=			None

						with zipfile.ZipFile(a__ ) as z:
												for filename in z.namelist():
																		if not os.path.isdir(a__ ):
																								# read the file
																								if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
																														with z.open(a__ ) as f:
																																				for line in f:
																																										lowerCAmelCase   :int						=			line.decode('UTF-8' ).strip()
																																										if filename == "failures_line.txt":
																																																try:
																																																						# `error_line` is the place where `error` occurs
																																																						lowerCAmelCase   :int						=			line[: line.index(': ' )]
																																																						lowerCAmelCase   :int						=			line[line.index(': ' ) + len(': ' ) :]
																																																						errors.append([error_line, error] )
																																																except Exception:
																																																						# skip un-related lines
																																																						pass
																																										elif filename == "summary_short.txt" and line.startswith('FAILED ' ):
																																																# `test` is the test method that failed
																																																lowerCAmelCase   :int						=			line[len('FAILED ' ) :]
																																																failed_tests.append(a__ )
																																										elif filename == "job_name.txt":
																																																lowerCAmelCase   :List[str]						=			line

						if len(a__ ) != len(a__ ):
												raise ValueError(
												    F"""`errors` and `failed_tests` should have the same number of elements. Got {len(a__ )} for `errors` """
												    F"""and {len(a__ )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"""
												    ' problem.' )

						lowerCAmelCase   :Optional[int]						=			None
						if job_name and job_links:
												lowerCAmelCase   :Dict						=			job_links.get(a__     ,      a__ )

						# A list with elements of the form (line of error, error, failed test)
						lowerCAmelCase   :Union[str, Any]						=			[x + [y] + [job_link] for x, y in zip(a__     ,      a__ )]

						return result


def        UpperCAmelCase      (					a__     ,      a__=None ):





						'''simple docstring'''






						lowerCAmelCase   :Any						=			[]

						lowerCAmelCase   :Optional[int]						=			[os.path.join(a__     ,      a__ ) for p in os.listdir(a__ ) if p.endswith('.zip' )]
						for p in paths:
												errors.extend(get_errors_from_single_artifact(a__     ,      job_links=a__ ) )

						return errors


def        UpperCAmelCase      (					a__     ,      a__=None ):





						'''simple docstring'''






						lowerCAmelCase   :int						=			Counter()
						counter.update([x[1] for x in logs] )
						lowerCAmelCase   :Tuple						=			counter.most_common()
						lowerCAmelCase   :Dict						=			{}
						for error, count in counts:
												if error_filter is None or error not in error_filter:
																		lowerCAmelCase   :Optional[int]						=			{'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}

						lowerCAmelCase   :Optional[Any]						=			dict(sorted(r.items()     ,      key=lambda a__ : item[1]["count"]     ,      reverse=a__ ) )
						return r


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			test.split('::' )[0]
						if test.startswith('tests/models/' ):
												lowerCAmelCase   :Union[str, Any]						=			test.split('/' )[2]
						else:
												lowerCAmelCase   :Optional[int]						=			None

						return test


def        UpperCAmelCase      (					a__     ,      a__=None ):





						'''simple docstring'''






						lowerCAmelCase   :str						=			[(x[0], x[1], get_model(x[2] )) for x in logs]
						lowerCAmelCase   :Any						=			[x for x in logs if x[2] is not None]
						lowerCAmelCase   :Tuple						=			{x[2] for x in logs}

						lowerCAmelCase   :Optional[Any]						=			{}
						for test in tests:
												lowerCAmelCase   :Union[str, Any]						=			Counter()
												# count by errors in `test`
												counter.update([x[1] for x in logs if x[2] == test] )
												lowerCAmelCase   :List[str]						=			counter.most_common()
												lowerCAmelCase   :str						=			{error: count for error, count in counts if (error_filter is None or error not in error_filter)}
												lowerCAmelCase   :Optional[Any]						=			sum(error_counts.values() )
												if n_errors > 0:
																		lowerCAmelCase   :Dict						=			{'count': n_errors, 'errors': error_counts}

						lowerCAmelCase   :Dict						=			dict(sorted(r.items()     ,      key=lambda a__ : item[1]["count"]     ,      reverse=a__ ) )
						return r


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Any						=			'| no. | error | status |'
						lowerCAmelCase   :Optional[int]						=			'|-:|:-|:-|'
						lowerCAmelCase   :Tuple						=			[header, sep]
						for error in reduced_by_error:
												lowerCAmelCase   :Optional[Any]						=			reduced_by_error[error]['count']
												lowerCAmelCase   :List[str]						=			F"""| {count} | {error[:1_00]} |  |"""
												lines.append(a__ )

						return "\n".join(a__ )


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Any						=			'| model | no. of errors | major error | count |'
						lowerCAmelCase   :int						=			'|-:|-:|-:|-:|'
						lowerCAmelCase   :int						=			[header, sep]
						for model in reduced_by_model:
												lowerCAmelCase   :Dict						=			reduced_by_model[model]['count']
												lowerCAmelCase	, lowerCAmelCase   :Any						=			list(reduced_by_model[model]['errors'].items() )[0]
												lowerCAmelCase   :Any						=			F"""| {model} | {count} | {error[:60]} | {_count} |"""
												lines.append(a__ )

						return "\n".join(a__ )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
		parser.add_argument(
		    '--output_dir',
		    type=str,
		    required=True,
		    help='Where to store the downloaded artifacts and other result files.',
		)
		parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
		__SCREAMING_SNAKE_CASE   =   parser.parse_args()

		os.makedirs(args.output_dir, exist_ok=True)

		__SCREAMING_SNAKE_CASE   =   get_job_links(args.workflow_run_id, token=args.token)
		__SCREAMING_SNAKE_CASE   =   {}
		# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
		# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
		if _job_links:
				for k, v in _job_links.items():
						# This is how GitHub actions combine job names.
						if " / " in k:
								__SCREAMING_SNAKE_CASE   =   k.find(' / ')
								__SCREAMING_SNAKE_CASE   =   k[index + len(' / ') :]
						__SCREAMING_SNAKE_CASE   =   v
		with open(os.path.join(args.output_dir, 'job_links.json'), 'w', encoding='UTF-8') as fp:
				json.dump(job_links, fp, ensure_ascii=False, indent=4)

		__SCREAMING_SNAKE_CASE   =   get_artifacts_links(args.workflow_run_id, token=args.token)
		with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
				json.dump(artifacts, fp, ensure_ascii=False, indent=4)

		for idx, (name, url) in enumerate(artifacts.items()):
				download_artifact(name, url, args.output_dir, args.token)
				# Be gentle to GitHub
				time.sleep(1)

		__SCREAMING_SNAKE_CASE   =   get_all_errors(args.output_dir, job_links=job_links)

		# `e[1]` is the error
		__SCREAMING_SNAKE_CASE   =   Counter()
		counter.update([e[1] for e in errors])

		# print the top 30 most common test errors
		__SCREAMING_SNAKE_CASE   =   counter.most_common(30)
		for item in most_common:
				print(item)

		with open(os.path.join(args.output_dir, 'errors.json'), 'w', encoding='UTF-8') as fp:
				json.dump(errors, fp, ensure_ascii=False, indent=4)

		__SCREAMING_SNAKE_CASE   =   reduce_by_error(errors)
		__SCREAMING_SNAKE_CASE   =   reduce_by_model(errors)

		__SCREAMING_SNAKE_CASE   =   make_github_table(reduced_by_error)
		__SCREAMING_SNAKE_CASE   =   make_github_table_per_model(reduced_by_model)

		with open(os.path.join(args.output_dir, 'reduced_by_error.txt'), 'w', encoding='UTF-8') as fp:
				fp.write(sa)
		with open(os.path.join(args.output_dir, 'reduced_by_model.txt'), 'w', encoding='UTF-8') as fp:
				fp.write(sa)